Image processing method for automatic image registration and correction

ABSTRACT

There is provided an image processing method for use in a computer system having a database for storing a reference image, control point chips (CP chips) of the reference image, and auxiliary data including geographic coordinates, incidence angles and orientation angles of the reference image and the CP chips, comprising the steps of (a) a CP chip extraction step of, when a target image and auxiliary data including location information and incidence and orientation angle information of the target image are inputted, finding out a region of the reference image corresponding to a region including the target image, selecting the found region as a control region, and extracting the CP chips included in the control region of the reference image, (b) an image matching step of searching matching points of the target image for the control points within the CP chips extracted from the CP chip extraction step, (c) a mismatching point removing step of finding out and removing mismatching points among matching results obtained from step (b), and (d) a precise image correction step of arranging the target image into a geographic coordinate system using the matching results obtained after performing step (c).

BACKGROUND OF THE INVENTION

[0001] 1. Description

[0002] The present invention relates to a method for processing asatellite image or general image, and more particularly, to a method forautomatically performing an image matching process, an imageregistration and a precise correction process.

[0003] 2. Background of the Invention

[0004] In general, remote sensing data measured from a satellite oraircraft include a great deal of errors and distortions due to aninstrument state upon measurement, an atmospheric condition, a movingdirection and posture of a platform, a map projection method, and thelike. Thus, these errors or distortions should be eliminated orcorrected for image restoration. To this end, geometric correction forcorrecting a difference between a geometric pattern of a collected imageand a reference map is generally performed.

[0005] This geometric correction is a process of correcting an inherentgeometric distortion of the image. The geometric correction is mainlydivided into system correction using a result of systematicallyanalyzing causes of the errors and distortions and precision correctionusing ground control points.

[0006] The system correction is a process of analyzing all the causes ofthe distortions such as an altitude change and shaking of the platformoccurring when collecting the image, optical characteristics of ameasuring instrument, topographic undulation, earth's rotation, and amap projection method, calculating an inverse transformation algorithmfor correcting the distortion of the collected image using the analysisresult, and restoring the collected image to its original state usingthe inverse transformation algorithm. The system correction has anadvantage in that all the distortions included in the sametransformation algorithm can be easily corrected if all the causes ofthe distortions of the collected image can be accurately analyzed sothat the inverse transformation algorithm can also be accuratelycalculated. However, there are still disadvantages in that it is noteasy to analyze all the causes of the distortions of the collected imageand it is also difficult to precisely correct the distortions of theimage in case of severe topographic undulation and high imageresolution.

[0007] The precision correction is a process of analyzing only adistortion degree of the collected image without considering thedistortion causes thereof, finding a correction formula which can beused to correlate the collected image with a reference map using theanalysis result, and correcting the image distortion using thecorrection formula. Here, the control point is a coordinate of aspecific position with the same shape which has been extracted from thecollected image and the reference map so as to match the image with thereference map.

[0008] The distortion correction using the control point has advantagesin that it can be used even when it is difficult to know or analyze thedistortion cause, and the image distortion can be corrected moreaccurately than the system correction if the control point may beaccurately extracted. However, there is a disadvantage in that if thecontrol point is incorrectly extracted, the accurate correction resultcannot be obtained. In the past, an operator has manually extracted thecontrol point while viewing the collected image and the reference mapwith his/her naked eyes. Thus, a correction result may vary greatlyaccording to a degree of skill of the operator who has extracted thecontrol point. Further, there is inconvenience in that the control pointshould be obtained again every measuring day even though the same areawould be measured. Therefore, many researches on methods forautomatically extracting the control point have been recently conducted.

[0009] The methods for automatically extracting the control pointdeveloped heretofore are largely divided into semi-automatic extractionmethods and fully automatic extraction methods.

[0010] One of the methods for semi-automatically extracting the controlpoint is disclosed in Korean Patent Laid-Open Publication No. 1999-47500(published on Jul. 5, 1999). According to the method disclosed in thepublication, if the operator selects four control points, a systemcalculates a transformation formula and an inverse transformationformula used for correcting the image using the selected four controlpoints, selects new additional control points, and finally corrects theimage. However, since the operator should initially select and inputfour or more control points, there still remains a problem of the priorart in that the correction result varies according to the degree ofskill of the operator, and much time and higher costs are needed forcorrecting the image.

[0011] On the other hand, as the methods of automatically extracting thecontrol points, there are a method of extracting image characteristicsand control points using a wavelet scheme, a method of automaticallyextracting the control points from Landsat satellite images, a method ofautomatically extracting the control points using a multispectral band,a method of automatically extracting the control points using a genericalgorithm, and an automatic geometric correction technique using a DTM(digital terrain model). These various methods of automaticallyextracting the control points describes only a process of automaticallyextracting the control point from the image, but they never suggest orteach a process of finding out and overcoming the errors included in theextracted control points. According to the aforementioned methods,therefore, the control point can be automatically extracted, but thereis also a problem in that it is difficult to perform the accurate imageregistration and the precise image correction due to the errors includedin the extracted control points.

SUMMARY OF THE INVENTION

[0012] An object of the present invention for solving the problems inthe prior art is to provide an image processing method by which controlpoints are automatically extracted and mismatched control points arethen removed from the extracted control points so that imageregistration and precise image correction can be accurately andaccurately performed.

[0013] According to an aspect of the present invention for achieving theobject, there is provided an image processing method for use in acomputer system having a database for storing a reference image, controlpoint chips (CP chips) of the reference image, and auxiliary dataincluding geographic coordinates, incidence angles and orientationangles of the reference image and the CP chips, comprising the steps of(a) a CP chip extraction step of, when a target image and auxiliary dataincluding location information and incidence and orientation angleinformation of the target image are inputted, finding out a region ofthe reference image corresponding to a region including the targetimage, selecting the found region as a control region, and extractingthe CP chips included in the control region of the reference image, (b)an image matching step of searching matching points of the target imagefor the control points within the CP chips extracted from the CP chipextraction step, (c) a mismatching point removing step of finding outand removing mismatching points among matching results obtained fromstep (b), and (d) a precise image correction step of arranging thetarget image into a geographic coordinate system using the matchingresults obtained after performing step (c).

[0014] Further, there is also provided a computer-readable recordingmedium in which a program for performing an image processing method ofthe present invention is recorded.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other objects, advantages and features of thepresent invention will become apparent from the following description ofa preferred embodiment given in conjunction with the accompanyingdrawings, in which:

[0016]FIG. 1 is a flowchart illustrating operating procedures of animage processing method according to the present invention;

[0017]FIG. 2 is a view showing a state where a control region of areference image is selected using auxiliary data on a target image;

[0018]FIG. 3 is a view showing a state where a search window is rotatedaccording to orientation angles of the reference image and the targetimage;

[0019]FIG. 4 is a view showing a SPOT satellite image of a Taejeon areafor illustrating an advantageous effect of the present invention,wherein (a) shows its reference image and (b) shows its target image;and

[0020]FIG. 5 is a view showing results obtained through processes offinding out matching points of the target image, removing mismatchingpoints from the found matching points, establishing a camera model, andperforming a precise correction according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0021] Hereinafter, a preferred embodiment of an image processing methodaccording the present invention will be explained in detail withreference to the accompanying drawings.

[0022] The present invention is an algorithm running in a generalcomputer system. The general computer system comprises a conventionalinput/output device, a microprocessor for controlling the system as awhole, a ROM for storing the image processing algorithm of the presentinvention and providing the microprocessor with the algorithm, a RAM forstoring temporary data produced while the microprocessor executes theimage processing algorithm of the present invention, and a database forstoring data needed for performing the image processing algorithm of thepresent invention.

[0023] The database includes a reference image, control point chips (CPchips) of the reference image, and auxiliary data. Here, the CP chipsmean images which are cut from the reference image in a predeterminedsize around control points. The auxiliary data mean records of asatellite state and an image state at the time of photographing asatellite image. The information included in the auxiliary data may beslightly different from one another depending on the kinds ofsatellites. However, the auxiliary data generally include information onwhen the satellite image has been photographed, information on thesatellite, and information on a tilt angle, incidence angle andorientation angle of the satellite image. The information on when thesatellite image has been photographed includes date and time whenphotographed. The information on the satellite includes orbitinformation such as a location and velocity of the satellite and briefcoordinate information (including an error range). The tilt angle of thesatellite image is a photographing angle when the satellite image isphotographed, the incidence angle is an angle between a camera and asurface of the earth, and the orientation angle is an angle between atrue north direction and a photographing direction. Besides, theauxiliary data may include other information such as a processing level,a calibration parameter, gain/offset values, and an image size.

[0024]FIG. 1 is a flowchart illustrating operating procedures of theimage processing method according to the present invention.

[0025] The image processing method of the present invention comprises animage matching step of finding out the matching points between a targetimage and the reference image, a mismatching point removing step ofremoving mismatching points from matching results of the two images, anda precise image correcting step of rearranging the target image in ageographic coordinate system.

[0026] In a technical field of image processing, image matching means aprocess of finding out points to be matched with one another from two ormore stereographic images on which an identical object is photographed.That is, the image matching process is a step of searching the databaseif the target image and its auxiliary data are inputted, and finding outthe reference image corresponding to an area including the relevanttarget image, thereby finding out the matching points between the targetimage and the reference image.

[0027] Now, the image matching step will be explained in detail. If thetarget image and the auxiliary data thereof are inputted through theconventional input/output device of the computer system (step S101), themicroprocessor selects a control region on the reference image using theauxiliary data of the target image (step S102). At this time, theauxiliary data of the target image is used as coordinate information onfour boundary corners of the target image. In a case where the targetimage is the satellite image, the satellite provides the coordinateinformation on the four boundary corners of the target image and theinformation on the error range as the auxiliary data, and then, themicroprocessor selects a region slightly exceeding the error range fromthe four boundary corners of the target image for the control region.For example, assuming that as shown in FIG. 2, the coordinates of thefour boundary corners of the target image are C1 to C4, respectively,and the error range is e, the control region A of the reference image isfinally selected as a region covering the error range from the fourboundary corners of the target image.

[0028] Next, the CP chips included in the control region of thereference image are searched (step S103). Then, in order to find outpositions corresponding to the CP chips from the target image, a searchregion of the target image is selected (step S104). To this end, adifference between brief position information and actual positioninformation of the target image is used in the present invention. Forexample, if a position error of the SPOT image is approximately ±2 kmand its resolution is 10 m, the search region of the target image isselected as a region of about 400-by-400 pixels corresponding to alength of 4 km in longitudinal and transverse directions from thecontrol point.

[0029] The microprocessor selects the search region of the target imageand then searches the positions corresponding to the CP chips of thereference image from the search region of the target image. At thistime, the reference image and the target image have been heretoforesearched using a rectangular search window having a fixed size. However,according to the present invention, size and shape of the search windowcan be modified depending on the photographed state of the reference andtarget images (step S105).

[0030] To this end, there can be provided a method of changing the sizeof the search window using the incidence angle, a method of changing thesize of the search window using the tilt angle, and a method of rotatingthe search window using the orientation angle.

[0031] According to the method of changing the size of the search windowusing the incidence angle, the size of the search window of the targetimage can be changed based on a scale factor s which is obtained bydividing a cosine value of the incidence angle of the target image by acosine value of the incidence angle of the reference image, as expressedin the following formula (1).

s=cos(incidence angle of target image)/cos(incidence angle of referenceimage)   (1)

[0032] The method of changing the size of the search window using thetilt angle is similar to the method of changing the size of the searchwindow using the incidence angle.

[0033] According to the method of rotating the search window using theorientation angle, a coordinate (X₀, Y₀) of a central point of thetarget image, a distance (dx, dy) of an arbitrary lattice point from thecentral point in x- and y-directions, and the orientation angle (Θ) ofthe target image are first substituted into the following formula (2),and a moved point (X_(new), Y_(new)) of the lattice point is thencalculated. Then, the lattice point in the target image is shifted bythe coordinate of the central point and rotated about the central pointwith respect to the reference image.

X _(new)=cos Θ×dx−sin Θ×dy+X ₀

Y _(new)=sin Θ×dx+cos Θ×dy+Y ₀   (2)

[0034] A state where the search window has been rotated according to theorientation angles of the reference image and the target image is shownin FIG. 3. In the figure, (a) shows the reference image including the CPchips, and (b) shows the target image. The image matching process is aprocess of finding out the same point as the control point of thereference image within the search region of the target image. At thistime, an operator measures similarity between the search window of thetarget image and the CP chips of the reference image while shifting thesearch window within the search region of the target image. If the sizeof the search window is adjusted according to the incidence angles ortilt angles of the two images and the search window is rotated accordingto the orientation angles of the two images, the search results can beimproved.

[0035] Then, the matching points, which are matched with the controlpoints of the reference image, are found out using a normalizedcross-correlation between the CP chips of the reference image and thesearch window of the target image (step S106). Therefore, the imagematching process is completed.

[0036] However, when the image matching process has been completed, theimage matching results are not perfectly accurate but include somemismatching points. The most of researches or methods performedheretofore have given priority to minimization of occurrence of themismatching points, whereas the present invention proposes a method ofultimately enhancing matching accuracy by finding out and removing themismatching points from the matching results.

[0037] The step of removing the mismatching points from the matchingresults of the target image will be hereinafter explained in detail.First, some (at least eight) matching results are selected at randomfrom the above total image matching results (step S107), and a cameramodel is established (step S108). The camera model is a relationshipbetween a geographic coordinate and an image coordinate calculatedaccording to a location and posture of the satellite at the time ofphotographing the image. If the camera model is established, row andcolumn coordinates of the image can be converted into a geographiccoordinate (X, Y, Z). A technique for establishing the camera model islargely categorized into a physical model, an abstract model, and ageneral model. In the present invention, the camera model is establishedthrough a DLT (direct linear transformation) model corresponding to theabstract model proposed by Gupta and Hartley.

[0038] In case of the DLT model employed in the present invention, eightcontrol points are extracted at random and matching results about theextracted control points are substituted into the following formula (3)so that m parameters of an M_(target) matrix of the target image arecalculated. This M_(target) matrix of the target image becomes thecamera model. If the matching results (C_(target), R_(target)) about theeight arbitrarily extracted control points (Xi_(ref), Y_(ref), Z_(ref))are substituted into the following formula (3), the m parameters of theM_(target) matrix can be calculated.

[0039] If the M_(target) matrix is calculated, i.e. the camera model isestablished, the transformation from the geographic coordinate to theimage coordinate can be made: $\begin{matrix}{{\begin{pmatrix}{\omega \quad C_{tanget}} \\R_{tanget} \\\omega\end{pmatrix} = {\begin{pmatrix}m_{11} & m_{12} & m_{13} & m_{14} \\m_{21} & m_{22} & m_{23} & m_{24} \\m_{31} & m_{32} & m_{33} & m_{34}\end{pmatrix}\begin{pmatrix}X_{ref} \\Y_{ref} \\Z_{ref} \\1\end{pmatrix}}},} & (3)\end{matrix}$

[0040] where C_(target) and R_(target) are the image coordinates of thematching points of the target image; X_(ref), Y_(ref) and Z_(ref) arethe geographic coordinates of the control points; and m parameters arethe M_(target) matrix of the target image.

[0041] Next, after the M_(target) matrix (camera model) has beencalculated as such, the accuracy of the camera model is calculated usingthe control points other than the eight control points used forestablishing the camera model (step S109). That is, the other controlpoints, which are not used for establishing the camera model, are usedfor calculating the, accuracy of the camera model, and are substitutedinto the formula (3) so that the calculation results are compared withthe matching results about the relevant control points. Based on thesimilarity between the calculation results about the other controlpoints through the formula (3) and the matching results of the othercontrol points and the number of the control points of which two resultsare similar to each other, the accuracy of the camera model iscalculated.

[0042] Then, steps S107 to S109 are again performed. That is, eightrandom control points and relevant matching results are newly selectedamong the image matching results, and a new camera model is againestablished. Then, an accuracy of the newly established camera model iscalculated using the control points other than the eight control pointswhich has have been used for establishing the new camera model. StepsS107 to S109 are repeated until the most accurate camera model is foundout (step S110). After the most accurate camera model has been foundout, the control points fitted to the relevant camera model and thematching results thereof are accepted and the other control pointsunfitted to the camera model and the matching results thereof areconsidered as being mismatched.

[0043] The process of establishing the camera model based on the controlpoints extracted at random and calculating the accuracy of the cameramodel using the other control points as described above in the presentinvention is referred to as a RANSAC (random sample consensus)algorithm.

[0044] Next, if the camera model of the target image is accuratelyestablished, the target image can be rearranged into the geographiccoordinate system by substituting the row and column coordinates and thecamera model of the target image into the above formula (3) (step S111).The process of rearranging the target image into the geographiccoordinate system as above is called precision geometric collection.

[0045] In order to verify the effectiveness of the present invention,the SPOT satellite images of three areas (Taejeon, Boryung, Cheonju) areutilized. The reference images R are first photographed, and the CPchips are extracted and stored. Then, the target images T arephotographed. Table 1 below shows the auxiliary data classifiedaccording to the respective SPOT satellite images. Since the sceneacquisition data of all the SPOT satellite images are different from oneanother, there are great differences between their earth surfacecharacteristics. TABLE 1 Area Taejeon Boryung Cheonju Scene AcquisitionR: 1997.10.14 R: 1997.3.1 R: 1997.10.14 Date T: 1997.11.15 T: 1997.11.15T: 1997.11.15 Incidence angle R: 29.7° R: −29.7° R: 29.7° T: 4.9° T:0.5° T: 4.9° Orientation angle R: 13.7° R: 8.1° R: 13.7° T: 11.3° T:10.9° T: 11.3° Number of control 21 20 16 points

[0046]FIG. 4 shows the SPOT satellite image of the Taejeon area, wherein(a) is the reference image thereof and (b) is the target image thereof.In FIG. 4(a), cross marks are control points, and a region within arange of ±200 pixels in a horizontal direction from each of the controlpoints is used as a CP chip. Comparing FIG. 4(a) with FIG. 4(b), it isconfirmed that the two images are remarkably different from each otheralthough the identical area has been photographed.

[0047] Table 2 below is a comparative table showing the results offinding out the matching points of the target image corresponding to thecontrol points of the reference to image. That is, Table 2 shows ratiosof the number of success to the number of failure upon finding out ofthe matching points corresponding to the control points according tocases where the search window is fixed into a rectangular shape such asin the prior art (C type) and where the size or rotation angle of thesearch window is changed using the incidence angle or the orientationangle (A type). TABLE 2 Area Type of search Taejeon Boryung Cheonjuwindow C type A type C type A type C type A type Similarity >0.8 5:0 7:04:0 5:0 4:0 7:0 0.6< Simi-  6:10 7:7 6:3 9:3 5:5 2:2 larity <0.8Similarity <0.6 Nothing Nothing 3:4 2:1 0:2 0:5 Total sum 11:10 14:7 13:7  16:4  9:7 9:7

[0048] From Table 2, regardless of the shape of the search window, thelarger the similarity between the reference image and the target imageis, the more accurately the matching points for the control points canbe found out. Further, if the search window of which size or rotationangle is modified according to the incidence angle or the orientationangle is used instead of using the search window of which size and shapeare fixed, a probability that the matching points for the control pointscan be accurately found is increased. However, if the similarity is nothigh, it can be confirmed that the matching results include themismatching points.

[0049] Table 3 shows results that the mismatching points are filteredout from the results of searching the matching points of the targetimage for the control points of the reference image using the RANSACalgorithm and the camera model is then established. Test area TaejeonBoryung Cheonju Number of control points used in 14 16 9 the modelingSearch results for the 7 4 7 mismatching points Modeling error 0.70 0.790.90

[0050] As can be seen from Table 3, if the RANSAC algorithm is used, themismatching points can be accurately filtered out up to 100%. Further,it is understood that there is no difference in accuracy between theactual camera model and the camera model of the present invention.However, there occurs a certain accuracy difference in the image of theCheonju area. It is considered as a phenomenon resulting from the smallnumber of the control points used.

[0051]FIG. 5 is a view showing results obtained through processes offinding out matching points of the target image, removing mismatchingpoints from the found matching points, establishing a camera model, andperforming a precise correction according to the present invention.

[0052] According to the aforementioned image processing method of thepresent invention, there is, an advantage in that if the reference imageand the CP chips are stored in the database, the matching points of thetarget image corresponding to the control points of the reference imagecan be automatically extracted without any manual works of the operator.Therefore, there is another advantage in that the image registration andprecise correction can be automatically and accurately performed sincethe mismatching points are removed from the extracted matching results.

[0053] Although the present invention has been described in connectionwith the preferred embodiment, the preferred embodiment is intended notto limit the invention but to exemplify a best mode of the presentinvention. It will be understood by those skilled in the art thatvarious changes, modifications or adjustments may be made theretowithout departing from the spirit and scope of the invention. Therefore,the present invention is defined only by the appended claims whichshould be construed as covering such changes, modifications oradjustments.

What is claimed is:
 1. An image processing method for use in a computersystem having a database for storing a reference image, control pointchips (CP chips) of the reference image, and auxiliary data includinggeographic coordinates, incidence angles and orientation angles of thereference image and the CP chips, comprising the steps of: (a) a CP chipextraction step of, when a target image and auxiliary data includinglocation information and incidence and orientation angle in formation ofthe target image are inputted, finding out a region of the referenceimage corresponding to a region including the target image, selectingthe found region as a control region, and extracting the CP chipsincluded in the control region of the reference image; (b) an imagematching step of searching matching points of the target image for thecontrol points within the CP chips extracted from the CP chip extractionstep; (c) a mismatching point removing step of finding out and removingmismatching points among matching results obtained from step (b); and(d) a precise image correction step of arranging the target image into ageographic coordinate system using the matching results obtained afterperforming step (c).
 2. The method as claimed in claim 1, wherein step(b) further comprises the steps of: (b1) selecting a search region and asearch window of the target image; (b2) searching locations havinghighest similarity by measuring the similarity between the search windowof the target image and the CP chips of the reference image whileshifting the search window within the search region of the target image;and (b3) finding out the matching points, which are matched with thecontrol points of the reference image, from the search window used tofind out the locations having the highest similarity.
 3. The method asclaimed in claim 2, wherein a size of the search window is changedaccording to incidence and tilt angles of the target and referenceimages.
 4. The method as claimed in claim 2, wherein the search windowis rotated according to the orientation angle of the target image. 5.The method as claimed in claim 2, wherein step (b3) is performed byfinding out the matching points of the target image, which are matchedwith the control points of the reference image, using a normalizedcross-correlation relationship between the CP chips and the searchwindow used to find out the locations having the highest similarity. 6.A method as claimed in claim 1, wherein step (c) further comprises thesteps of: (c1) selecting a portion of the matching results about thepredetermined number of control points among the total matching resultsobtained from step (b) and establishing a camera model through theselected portion of the matching results; (c2) calculating accuracy ofthe camera model using matching results about the other control pointsother than the selected portion of the matching results used toestablish the camera model; (c3) selecting again the selected portion ofthe matching results used to establish the camera model and performingrepeatedly steps (c1) and (c2); and (c4) extracting a camera modelhaving the highest accuracy after performing step (c3), recognizingmatching results, which are not matched with the highest accurate cameramodel, as the mismatching points, and removing the recognized matchingresults.
 7. The method as claimed in claim 6, wherein in step (c1), mparameters of a M_(target) matrix is calculated by substituting at leasteight control points and the matching points corresponding to thecontrol points into the following formula (A): $\begin{matrix}{{\begin{pmatrix}{\omega \quad C_{tanget}} \\R_{tanget} \\\omega\end{pmatrix} = {\begin{pmatrix}m_{11} & m_{12} & m_{13} & m_{14} \\m_{21} & m_{22} & m_{23} & m_{24} \\m_{31} & m_{32} & m_{33} & m_{34}\end{pmatrix}\begin{pmatrix}X_{ref} \\Y_{ref} \\Z_{ref} \\1\end{pmatrix}}},} & (A)\end{matrix}$

where C_(target) and R_(target) are image coordinates of the matchingpoints of the target image; X_(ref), Y_(ref) and Z_(ref) are thegeographic coordinates of the control points corresponding to thematching points; and the m parameters are the M_(target) matrix of thetarget image, i.e. the camera model.
 8. The method as claimed in claim7, wherein in step (c2), the matching results about the other controlpoints and the camera model are substituted into the above formula (A),and thus, the accuracy of the camera model is calculated using thesimilarity between calculation results and the matching results and thenumber of control points by which the two similar results can beobtained.
 9. The method as claimed in claim 6, wherein in step (d), theimage coordinate of the target image is arranged into the geographiccoordinate system through the most accurate camera model.
 10. Acomputer-readable recording medium in which a program for use in acomputer system having a database for storing a reference image, controlpoint chips (CP chips) of the reference image, and auxiliary dataincluding geographic coordinates, incidence angles and orientationangles of the reference image and the CP chips is recorded, said programcomprising instructions for causing the computer system to implement:(i) a CP chip extraction step of, when a target image and auxiliary dataincluding location information and incidence and orientation angleinformation of the target image are inputted, finding out a region ofthe reference image corresponding to a region including the targetimage, selecting the found region as a control region, and extractingthe CP chips included in the control region of the reference image; (ii)an image matching step of searching matching points of the target imagefor the control points within the CP chips extracted from the CP chipextraction step; (iii) a mismatching point removing step of finding outand removing mismatching points among matching results obtained fromstep (ii); and (iv) a precise image correction step of arranging thetarget image into a geographic coordinate system using the matchingresults obtained after performing step (iii).